Spatial high-utility itemset (SHUI) mining is a significant big data analysis technique. It aims to locate all geographically interesting itemsets with high utility in a spatiotemporal database. An SHUI-Miner algorithm was presented in the literature to find the desired itemsets. Unfortunately, this algorithm suffered from performance issues when dealing with high-dimensional spatiotemporal databases. Based on this finding, this paper extends the state-of-the-art method by proposing a novel algorithm known as the high-dimensional SHUI-miner (HDSHUI-Miner). Our algorithm explores several novel pruning strategies to decrease the search space and computational cost required to find the desired itemsets. Experimental results obtained on seven real-world databases demonstrate that HDSHUI-Miner outperforms SHUI-Miner with respect to memory consumption, runtime, and scalability. Finally, we present two real-world case studies to illustrate the usefulness of the proposed algorithm.